A stroke is a serious life-threatening condition and a leading cause of death and disability that happens when the blood vessels to part of the brain are blocked or burst. While major advances in the understanding of the ischemic cascade in stroke was made over several decades, limited therapeutic options and high mortality and disability have caused researchers to extend the focus toward peripheral changes beyond brain. The largest proportion of microbes in human body reside in the gut and the interaction between host and microbiota in health and disease is well known. Our study aimed to explore the gut microbiota in patients with stroke with comparison to control group. Fecal samples were obtained from 51 subjects: 25 stroke patients (18 hemorrhagic, 7 ischemic) and 26 healthy control subjects. The variable region V3–V4 of the 16S rRNA gene was sequenced using the Illumina MiSeq platform. PICRUSt2 was used for prediction of metagenomics functions. Our results show taxonomic dysbiosis in stroke patients in parallel with functional dysbiosis. Here, we show that stroke patients have (1) increased Parabacteroides and Escherichia_Shigella, but decreased Prevotella and Fecalibacterium; (2) higher transposase and peptide/nickel transport system substrate-binding protein, but lower RNA polymerase sigma-70 factor and methyl-accepting chemotaxis protein, which are suggestive of malnutrition. Nutrients are essential regulators of both host and microbial physiology and function as key coordinators of host–microbe interactions. Manipulation of nutrition is expected to alleviate gut dysbiosis and prognosis and improve disability and mortality in the management of stroke.

Molecular characterization technology in genetically modified organisms, in addition to how transgenic biotechnologies are developed now require full transparency to assess the risk to living modified and non-modified organisms. Next generation sequencing (NGS) methodology is suggested as an effective means in genome characterization and detection of transgenic insertion locations. In the present study, we applied NGS to insert transgenic loci, specifically the epidermal growth factor (EGF) in genetically modified rice cells. A total of 29.3 Gb (~72× coverage) was sequenced with a 2 × 150 bp paired end method by Illumina HiSeq2500, which was consecutively mapped to the rice genome and T-vector sequence. The compatible pairs of reads were successfully mapped to 10 loci on the rice chromosome and vector sequences were validated to the insertion location by polymerase chain reaction (PCR) amplification. The EGF transgenic site was confirmed only on chromosome 4 by PCR. Results of this study demonstrated the success of NGS data to characterize the rice genome. Bioinformatics analyses must be developed in association with NGS data to identify highly accurate transgenic sites.

MicroRNAs (miRNAs) are a class of small RNAs that post-transcriptionally regulate gene expression in animals and plants. The recent rapid advancement in miRNA biology, including high-throughput sequencing of small RNA libraries, inspired the development of a bioinformatics software, miRAuto, which predicts putative miRNAs in model plant genomes computationally. Furthermore, miRAuto enables users to identify miRNAs in non-model plant species whose genomes have yet to be fully sequenced. miRAuto analyzes the expression of the 5′-end position of mapped small RNAs in reference sequences to prevent the possibility of mRNA fragments being included as candidate miRNAs. We validated the utility of miRAuto on a small RNA dataset, and the results were compared to other publicly available miRNA prediction programs. In conclusion, miRAuto is a fully automated user-friendly tool for predicting miRNAs from small RNA sequencing data in both model and non-model plant species. miRAuto is available at http://nature.snu.ac.kr/software/miRAuto.htm.

Non-Invasive Prenatal Testing (NIPT) is a technique that allows pregnant women to screen for chromosomal abnormalities in their developing fetus without the need for invasive procedures like amniocentesis or chorionic villus sampling. However, current methods to detect anomaly from maternal cell-free DNAs (cfDNAs) that are based on the sequence read counts calculating z-scores face challenges with false positives and negatives. To address these challenges, we aimed to develop a novel NIPT algorithm named KF-NIPT, which is derived from the initials of k-mer and fetal fraction used in its development with the goal of significantly improving accuracy. We developed a KF-NIPT, a new algorithm that estimate chromosomal anomaly by calculating K-mer-based sequence depth and fetal fraction from the whole genome sequencing (WGS) data. Moreover, we implemented a modified preprocessing pipeline for the WGS data, correcting the biases of the genomic mapping quality and the GC contents. The performance of our method was evaluated using publicly available NIPT data. We could demonstrate that our method has better accuracy and sensitivity compared to those of the previous methods. We found that using k-mer and fetal fraction reduces errors in NIPT and have integrated this into a pipeline, showing that the traditional read count-based z-score method can be improved. KF-NIPT is implemented in the R and Python environment. The source code is available at https://github.com/eastbrain/KF-NIPT . KF-NIPT has been tested on Ubuntu Linux-64 server and Linux-64 on Windows using a WSL (Windows Subsystem for Linux).

The monitoring and surveillance of antimicrobial resistance (AMR) is an important procedure in clinical patient management and epidemiological public health. Conventionally, culture-based tools such as disk diffusion methods or broth dilution methods for antibiotic susceptibility tests are used. While culture-independent approaches, such as PICRUSt2, Tax4Fun, or MicFunPred, have recently been tried based on predictive functional profiling using the 16S rRNA marker gene, evaluations of AMR tools are scarce. A total of 20 E. coli strains (Carbapenem-resistant (CRE) positive: 10, CRE negative: 10) were used. The AMR phenotype was based on Vitek2 (bioMerieux). DNA was extracted from the 20 strains, and 16S rRNA (V3-V4 region) and shotgun sequencing was carried out. The bioinformatic pipelines were QIIM2 for 16S rRNA and MetaPhlAn4 for shotgun. The functional prediction tools were PICRUSt2, Tax4Fun, and MicFunPred for 16S rRNA and AMRFinderPlus for shotgun. The presence/absence of 23 KEGG numbers regarding AMR in PICRUSt2, Tax4Fun, and MicFunPred were compared to shotgun AMR profiles. The F1 scores were calculated according to each 16S marker gene-based prediction tool using a confusion matrix. A total of 12 classes of antibiotics, including carbapenem, were analyzed. The F1 scores of 16S predictive functional profilers regarding AMR were 0.22 for Tax4Fun, 0.12 for PICRUSt2, and 0.08 for MicFunPred. While Tax4Fun showed the highest F1 score of the three 16S predictive functional profilers, the F1 scores were generally low. Our study highlights the necessity of integrating specialized AMR databases and improving algorithmic approaches to achieve meaningful accuracy in resistance prediction.

16S rRNA amplicon sequencing has been an effective method for profiling microbial taxonomy in microbiome research, as it offers lower per-sample costs and higher sample throughput than shotgun metagenomics. Although 16S rRNA sequencing offers clear advantages over shotgun sequencing, it depends on precise trimming of low-quality bases at the 3′ ends of reads. Given the widespread use of 16S rRNA amplicon sequencing, there is an increasing demand for analysis tools that can identify errors in the 3′ region of reads and remove erroneous bases. While various algorithms for predicting trim locations are widely employed, most are command-line standalone tools, which pose challenges for users with limited computational background or resources. Furthermore, in the absence of biological or experimental priors such as amplicon size, trim position predictions may be unreliable. Here, we introduce PixelCut, a fully automated trim-position prediction framework that requires no hyperparameters or prior biological information for accurate prediction. Unlike most available algorithms that operate on raw FASTQ data, PixelCut analyzes the per-base quality report generated by FastQC to infer trimming positions. Based on the recommended quality score threshold from the quality report, PixelCut inspects the quality scores across bases and automatically determines the recommended trim position using character recognition techniques based on computer vision. We have also developed a user-friendly web application to make the method accessible to those without programming expertise, while offering a command-line version for advanced users. Through comprehensive computer simulations, we show that PixelCut produces taxonomic profiling results that are consistent with those from popular trim-location prediction algorithms.